CN106529428A - Underwater target recognition method based on deep learning - Google Patents

Underwater target recognition method based on deep learning Download PDF

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CN106529428A
CN106529428A CN201610929714.XA CN201610929714A CN106529428A CN 106529428 A CN106529428 A CN 106529428A CN 201610929714 A CN201610929714 A CN 201610929714A CN 106529428 A CN106529428 A CN 106529428A
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曹旭
张效民
于洋
韩鹏
罗建
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Northwestern Polytechnical University
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Abstract

The present invention relates to an underwater target recognition method based on deep learning, which mainly solves the problem that the existing target recognition system mainly relies on the shallow model to extract the artificial feature, resulting in the recognition accuracy is not high. The method comprises the following concrete steps that (1) power spectrum characteristics of original ship target radiated noise data are obtained in segment; (2) the power spectrum characteristics are divided into a training data set and a testing data set; (3) training data are subjected to ZCA whitening pretreatment; (4) a stack-type self-coding network is constructed; (5) a deep network node is subjected to fine tuning; and (6) testing data are subjected to classification and recognition. Sea test data and experiments show that the deep network is utilized to learn deep characteristics of ship target radiated noise for performing classification recognition, the recognition rate reaches over 94%, and the underwater target recognition effect is improved, which has an important real application prospect for future recognition and monitoring of underwater ships and marine organisms.

Description

Underwater targets recognition based on deep learning
Technical field
The invention belongs to field of underwater acoustic signal processing, is related to pattern recognition and artificial intelligence, particularly should by deep learning For the feature extraction to method of underwater vessel radiated noise, can be used for passive sonar system to naval vessel and the long distance of other submarine targets From identification.
Background technology
It is modern that the acoustical signal of radiation under water produced when being navigated by water using Ship Target carries out the automatic target detection of passive type The important component part of naval vessels and intelligent underawater ordnance.However, on the one hand due to being related to military field, research institution of various countries is to each Of a relatively high from achievement in research confidentiality, the visible effective means of identification of open source literature is less；On the other hand due to marine environment Complexity and underwater acoustic channel particularity, show as：Underwater acoustic channel when space-variant, non-linear and non-Gaussian system, reverberation with And Environmental Noise Influence etc., to efficiently extract from the Ocean acoustics signals under Low SNR and can characterize target classification Substitutive characteristics, inherently one technical barrier.These factors cause the Classification and Identification of automatic target under water of passive type to become But developed relatively slow recognized problem by numerous scholars and research worker concern.
Resolve how it is critical only that effectively from different perspectives to the original letter of target for passive target Study of recognition under water Number it is analyzed and studies.In recent years, substantial amounts of related academic documents and patented technology have been emerged.Wherein, conventional means master The following aspects to be concentrated on：Based on time domain plethysmographic signal structure zero crossing distribution, peak-to-peak amplitude distribution, wavelength difference distribution and The target classification identification that the characteristic vectors such as wave train area distributions are carried out.Based on traditional Power estimation, super-resolution Power estimation and high-order The target classification that the characteristic vectors such as Ship Target line spectrum, continuous spectrum, modulated spectra and spectrum shape that Power estimation technology is extracted are carried out Identification.Based on nonlinear theories such as chaos, point shapes, it is original data space with time serieses, carries out phase space reconfiguration or divide Number Brownian movement modeling, by the Classification and Identification for calculating connection dimension, Lyapunov indexes or Hurst indexes are carried out.Based on short When Fourier transformation, Gabor transformation, wavelet transformation and quadratic form when-frequency division cloth includes Cohen alanysis, Wigner- The target classification identification that the time and frequency domain characteristics that the time frequency analysis means such as Ville distributions and Choi-Williams distributions are extracted are carried out Deng.
Above-mentioned target identification method mainly extracts clarification of objective using Heuristicses, and their performance is largely determined Selection due to the degree of association that operator is presented for the degree of awareness and sample of sample, experiment parameter and model easily receives people's Subjective impact, generalization are restricted.Meanwhile, above method is based primarily upon shallow Model and carries out feature extraction, it is difficult to reflect mesh The most essential internal relation of mark, causes the target characteristic for only obtaining by shallow Model met under practical situation to naval vessel mesh Target is effectively recognized.Depth model is taken to carry out the new direction that feature learning is Underwater Targets Recognition.
The content of the invention
Technical problem to be solved
It is an object of the invention to overcome the artificial limitation for extracting target characteristic, it is proposed that a kind of based on deep learning Underwater targets recognition.Learn the implicit structure in Ship Target radiated noise time-frequency spectrum using sparse self-encoding encoder, will be dilute Thin own coding is cascaded as stack own coding neutral net, and combination low-dimensional feature obtains the higher-dimension of target and represents, recycles softmax Grader is classified to target.Due to carrying out unsupervised learning using depth network, it is not only avoided that artificial extraction feature Limitation and subjectivity, moreover it is possible to the relation of signal spectrum " part overall " is represented in overall compact mode, it is special to target Property has more essential portraying.Simultaneously as supervised learning is carried out using softmax graders, can further improve identification Accuracy, reaches to true ship seakeeping probability more than 94%.
Technical scheme
A kind of Underwater targets recognition based on deep learning, it is characterised in that step is as follows：
Step 1：Power spectrum characteristic is asked for original Ship Target radiated noise data sectional：
Obtain original Ship Target radiated noise sequence, carry out segment processing, the segment data for obtaining be x (n), n=0, 1,2 ..., N-1, according to formulaPower Spectral Estimation is carried out to segment data, by adjacent 4 segment data Power spectrum carry out averagely, obtaining final power spectrum characteristic result of calculation：
Wherein f=(f1,f2,f3,…,fM) represent corresponding discrete point in frequency；
Step 2：Power spectrum characteristic is divided into into training dataset and test data set：
By power spectrum characteristic P (f) according to 3:1 randomly selects, and is divided into training dataset and test data set, will train number According to each frame power spectrum characteristic of collection as string, training matrix P is constituted：
Wherein, Pi(fj) represent that the jth of i-th training data ties up power spectrum characteristic；
Step 3：ZCA whitening pretreatments are carried out to training data；
Step 4：Construct and train stack autoencoder network；
Step 5：Fine setting fine-tune depth network model：
Using back-propagation algorithm, all layers of stack autoencoder network are regarded into a model, it is in each iteration, excellent Change all weighted values in network, obtain final network node parameter；
Step 6：Classification and Identification is carried out to test data：
According to the variance of the characteristic mean of training dataset, pretreatment test data, using the data for having processed as stack The input of autoencoder network, calculates final classification results.
ZCA whitening pretreatments carried out to training data comprise the following steps that described in step 3：
The first step：Training data is standardized, the average of kth dimensional feature is first calculated Seek the standard deviation of kth dimensional featureCan according to the following formula normalized process after training Data：
Second step：ZCA whitening pretreatments are carried out, the covariance matrix of sample is first calculatedSingular value is done to Σ Characteristic vector is tried to achieve in decomposition, is discharged by leu license-master time order, is constituted matrix U=[u1,u2,…,un], by eigenvalue by diagonal Arrangement, composition characteristic value diagonal matrix S can be calculated as follows final ZCA albefaction results：
Wherein, ε is used as regularization parameter.
Construction described in step 4 simultaneously trains comprising the following steps that for stack autoencoder network：
The first step：Construction stack autoencoder network, constructs stack autoencoder network using sparse self-encoding encoder, and stack is certainly The hidden layers numbers of coding network are 3, and the interstitial content of hidden layer is respectively 100-100-50, nodes and the ZCA albefactions of input layer Pretreated training data intrinsic dimensionality is mutually all 512, and finally, increasing by one layer of softmax grader is used for output of classifying, always Classification number be 3, the nodes of whole network are 512-100-100-50-3；
Second step：Using the training data X after ZCA whitening pretreatmentsZCATraining stack autoencoder network, first, according to instruction The method of the single sparse self-encoding encoder of white silk, using XZCAAs input, the node parameter W of first hidden layer is calculated(1,1)And b(1,1), Concrete grammar is as follows：
Step 1) for the input data set X comprising L sampleZCA, define the overall cost function of sparse self-encoding encoder
Wherein, λ is weight attenuation parameter, is set to 3e-3, and β is the parameter for controlling openness penalty factor, and being set to 3, KL is Relative entropy；
Step 2) by W(1,1)And b(1,1)Random initializtion is one be close to zero value, using L-BFGS iterative algorithms to generation Valency function is optimized, and iterationses are set to 100, is met the optimal solution of requirement；
3rd step：Using the activation value of the first hidden layer as the input of the second hidden layer, the second hidden layer is calculated with same method Node parameter W(2,1)And b(2,1), by that analogy, successively train, obtain the parameter of all hidden layers；
4th step：Last hidden layer is exported into the input as softmax graders, by L-BFGS iterative algorithm iteration The optimal solution for obtaining softmax graders 100 times.
Beneficial effect
A kind of Underwater targets recognition based on deep learning proposed by the present invention, compared with prior art with following Advantage：
First, compared with traditional feature extracting method, present invention, avoiding it is artificial extract feature, but using it is sparse from Encoder learns to input sample, by the unsupervised learning ability of self-encoding encoder, automatically excavate target input sample it Between internal relation, so as to learn more effective feature.
Second, sparse self-encoding encoder is stacked by the present invention, constructs stack autoencoder network, using deep learning to complicated letter Several powerful expression abilities, from low-dimensional feature takes out high dimensional feature, represents signal spectrum " portion in overall compact mode It is point overall " relation, have more essential portraying to target property.
3rd, the fine setting that the present invention carries out having supervision to the network after pre-training using softmax graders is further carried High discrimination.
Description of the drawings
Fig. 1 is the overall procedure block diagram of the Underwater targets recognition based on deep learning
Fig. 2 is the part diagram of training dataset
Fig. 3 is the part diagram of pretreated training data
Fig. 4 is the elliptical structure figure of stack autoencoder network
Fig. 5 is that corresponding visualization feature exports diagram after 100 hidden nodes of first hidden layer are trained
Fig. 6 is the feature output diagram of a certain node of first hidden layer
Specific embodiment
In conjunction with embodiment, accompanying drawing, the invention will be further described：
Overall procedure based on the Underwater targets recognition of deep learning is as shown in figure 1, comprise the following steps that：
(1) power spectrum characteristic is asked for original Ship Target radiated noise data sectional.
Original Ship Target radiated noise sequence is obtained, the sample frequency of signal is 4KHz, carries out segment processing, is divided Segment data x (n), n=0,1,2 ..., N-1, according to formulaPower spectrum is carried out to segment data to estimate Meter, the power spectrum of adjacent 4 segment data is carried out averagely, obtaining final power spectrum characteristic result of calculation：
Wherein f=(f1,f2,f3,…,fM) represent corresponding discrete point in frequency.It is main due to ships radiated noise signal Spectrum component is concentrated mainly on 0-2KHz, and the points N for calculating FFT is set to the intrinsic dimensionality M of 1024, i.e. one-sided power spectrum herein For 512, spectral resolution is 4Hz.
(2) power spectrum characteristic is divided into into training dataset and test data set.
By all one-sided power spectrum characteristics according to 3:1 randomly selects, and is divided into training dataset and test data set, Using each frame power spectrum characteristic of training dataset as string, training matrix P is constituted：
Wherein, Pi(fj) represent that the jth of i-th training data ties up power spectrum characteristic.Accompanying drawing 2 is represented for the part of matrix P.
(3) ZCA whitening pretreatments are carried out to training data；
The first step, is standardized to training data, first calculates the average of kth dimensional feature Seek the standard deviation of kth dimensional featureCan according to the following formula normalized process after training Data：
Second step, carries out ZCA whitening pretreatments, first calculates the covariance matrix of sampleSingular value is done to Σ Characteristic vector is tried to achieve in decomposition, is discharged by leu license-master time order, and is constituted matrix U=[u1,u2,…,un], by eigenvalue by right Angle arranges, and composition characteristic value diagonal matrix S can be calculated as follows final ZCA albefaction results：
Wherein, ε is used as regularization parameter.Accompanying drawing 3 is through pretreated matrix XZCAPart represent.
(4) construct and train stack autoencoder network.
The first step：Construction stack autoencoder network, constructs stack autoencoder network using sparse self-encoding encoder, and stack is certainly The hidden layers numbers of coding network are 3, and the interstitial content of hidden layer is respectively 100-100-50, nodes and the ZCA albefactions of input layer Pretreated training data intrinsic dimensionality is mutually all 512, and finally, increasing by one layer of softmax grader is used for output of classifying, always Classification number be 3, the nodes of whole network are 512-100-100-50-3.Accompanying drawing 4 shows for the omission of stack autoencoder network It is intended to.
Second step, using the training data X after ZCA whitening pretreatmentsZCATraining stack autoencoder network, first, according to instruction The method of the single sparse self-encoding encoder of white silk, using XZCAAs input, the node parameter W of first hidden layer is calculated(1,1)And b(1,1), Concrete grammar is as follows：
Step 1. is for the input data set X comprising L sampleZCA, define the overall cost function of sparse self-encoding encoder
Wherein, λ is weight attenuation parameter, is set to 3e-3, and β is the parameter for controlling openness penalty factor, and being set to 3, KL is Relative entropy.
Step 2. is by W(1,1)And b(1,1)Random initializtion is one be close to zero value, using L-BFGS iterative algorithms to generation Valency function is optimized, and iterationses are set to 100, is met the optimal solution of requirement.Accompanying drawing 5 is 100 of the first hidden layer hidden After node layer training, corresponding visualization feature output, is divided into 100 row and shows.Accompanying drawing 6 is the single feature of wherein a certain node Output, " spike " in figure represent node and line-spectrum detection are carried out on different Frequency points.
3rd step, using the activation value of the first hidden layer as the input of the second hidden layer, calculates the second hidden layer with same method Node parameter W(2,1)And b(2,1), by that analogy, successively train, obtain the parameter of all hidden layers.
Last hidden layer is exported the input as softmax graders, by L-BFGS iterative algorithm iteration by the 4th step The optimal solution for obtaining softmax graders 100 times.
(5) finely tune depth network model.
Based on back-propagation algorithm, (fine-tune) is finely adjusted to the stack autoencoder network after pre-training, is obtained most Whole network node parameter.
(6) Classification and Identification is carried out to test data.
According to the variance of the characteristic mean of training dataset, pretreatment test data, using the data for having processed as stack The input of autoencoder network, calculates final classification results.
The effect of the present invention is illustrated by following experiment embodiment.Experimental data is from certain carried out in South China Sea Test on secondary sea.The naval vessel of A types, Type B, c-type three types is used respectively as object ship, and hydrophone lays depth for 50 Rice, each type of naval vessel are navigated by water according to left and right by way of in different beam positions, and data acquisition module sample frequency sets It is set to 4KHz.Table 1 below gives the recognition result that target recognition is carried out by the method for the present invention.As can be seen from Table 1, for Three kinds of targets, method of the present invention recognition result reach more than 94%.
1 the inventive method recognition result of table
Target recognition is carried out to experimental data using RBF-SVM and BPNN, is contrasted with the method for the present invention, as a result such as Shown in table 2.As can be seen from Table 2, the discrimination of the method that the present invention is adopted is higher than the RBF-SVM and BPNN of current popular, Discrimination difference high 4.1% and 5.7%.
2 three kinds of method recognition result contrasts of table
Above test result indicate that, proposed by the invention is effectively may be used based on the Underwater targets recognition of deep learning Capable, it is possible to increase the identification probability to Ship Target, undersea long is detected and is recognized with important practical significance.

Claims (3)

1. a kind of Underwater targets recognition based on deep learning, it is characterised in that step is as follows：
Step 1：Power spectrum characteristic is asked for original Ship Target radiated noise data sectional：
Obtain original Ship Target radiated noise sequence, carry out segment processing, the segment data for obtaining be x (n), n=0,1, 2 ..., N-1, according to formulaPower Spectral Estimation is carried out to segment data, by adjacent 4 segment data Power spectrum is carried out averagely, obtains final power spectrum characteristic result of calculation：
P ( f ) = 1 4 Σ i = 1 4 P i ( f )
Wherein f=(f1,f2,f3,…,fM) represent corresponding discrete point in frequency；
Step 2：Power spectrum characteristic is divided into into training dataset and test data set：
By power spectrum characteristic P (f) according to 3:1 randomly selects, and is divided into training dataset and test data set, by training dataset Each frame power spectrum characteristic as string, constitute training matrix P：
Wherein, Pi(fj) represent that the jth of i-th training data ties up power spectrum characteristic；
Step 3：ZCA whitening pretreatments are carried out to training data；
Step 4：Construct and train stack autoencoder network；
Step 5：Fine setting fine-tune depth network model：
Using back-propagation algorithm, all layers of stack autoencoder network are regarded into a model, in each iteration, optimize net All weighted values in network, obtain final network node parameter；
Step 6：Classification and Identification is carried out to test data：
According to the variance of the characteristic mean of training dataset, pretreatment test data will be the data for having processed self-editing as stack The input of code network, calculates final classification results.
2. a kind of Underwater targets recognition based on deep learning according to claim 1, it is characterised in that step 3 institute That what is stated carries out ZCA whitening pretreatments to training data and comprises the following steps that：
The first step：Training data is standardized, the average of kth dimensional feature is first calculatedAsk The standard deviation of kth dimensional featureCan according to the following formula normalized process after training number According to：
Second step：ZCA whitening pretreatments are carried out, the covariance matrix of sample is first calculatedSingular value decomposition is done to ∑ Characteristic vector is tried to achieve, is discharged by leu license-master time order, is constituted matrix U=[u1,u2,…,un], by eigenvalue by diagonal arrangement, Composition characteristic value diagonal matrix S, can be calculated as follows final ZCA albefaction results：
X Z C A = U · U T · X S + ϵ
Wherein, ε is used as regularization parameter.
3. a kind of Underwater targets recognition based on deep learning according to claim 1, it is characterised in that step 4 institute The construction stated simultaneously trains comprising the following steps that for stack autoencoder network：
The first step：Construction stack autoencoder network, constructs stack autoencoder network, stack own coding using sparse self-encoding encoder The hidden layers numbers of network are 3, and the interstitial content of hidden layer is respectively 100-100-50, and nodes and the ZCA albefactions of input layer are located in advance Training data intrinsic dimensionality after reason is mutually all 512, and finally, increasing by one layer of softmax grader is used for output of classifying, total class Shuo not be 3, the nodes of whole network are 512-100-100-50-3；
Second step：Using the training data X after ZCA whitening pretreatmentsZCATraining stack autoencoder network, it is first, single according to training The method of individual sparse self-encoding encoder, using XZCAAs input, the node parameter W of first hidden layer is calculated(1,1)And b(1,1), specifically Method is as follows：
Step 1) for the input data set X comprising L sampleZCA, define the overall cost function of sparse self-encoding encoder
J ( W , b ) = [ 1 L Σ i = 1 L ( 1 2 | | h W , b ( x ( i ) - y ( i ) ) | | 2 ) ] + λ 2 ( W ( 1 ) ) 2 + β Σ j = 1 s 2 K L ( ρ | | ρ ^ j )
Wherein, λ is weight attenuation parameter, is set to 3e-3, and β is the parameter for controlling openness penalty factor, and it is relative to be set to 3, KL Entropy；
Step 2) by W(1,1)And b(1,1)Random initializtion is one be close to zero value, using L-BFGS iterative algorithms to cost function It is optimized, iterationses are set to 100, is met the optimal solution of requirement；
3rd step：Using the activation value of the first hidden layer as the input of the second hidden layer, the section of the second hidden layer is calculated with same method Point parameter W(2,1)And b(2,1), by that analogy, successively train, obtain the parameter of all hidden layers；
4th step：Using last hidden layer output as softmax graders input, by L-BFGS iterative algorithms iteration 100 times Obtain the optimal solution of softmax graders.
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Application publication date: 20170322